Method and system for confident anomaly detection in computer network traffic

ABSTRACT

The present invention relates to systems and methods for detecting anomalies in computer network traffic with fewer false positives and without the need for time-consuming and unreliable historical baselines. Upon detection, traffic anomalies can be processed to determine valuable network insights, including health of interfaces, devices and network services, as well as to provide timely alerts in the event of attack.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and is a Continuation ofapplication Ser. No. 14/627,963, filed Feb. 20, 2015, of the same title,which application claims the benefit of provisional application No.61/987,440, May 1, 2014, of the same title, which applications areincorporated herein in their entirety by this reference.

Application Ser. No. 14/627,963 also claims the benefit of and is aContinuation-in-Part of non-provisional application Ser. No. 13/669,235,filed Nov. 5, 2012, entitled “A Streaming Method and System forProcessing Network Metadata”, now U.S. Pat. No. 9,392,010, whichapplication is a non-provisional of both provisional application No.61/556,817 filed on Nov. 7, 2011, same title, and No. 61/699,823, filedSep. 11, 2012, same title, which applications are incorporated herein intheir entirety by this reference.

Application Ser. No. 14/627,963 additionally claims the benefit of andis a Continuation-in-Part of non-provisional application Ser. No.13/830,924, filed Mar. 14, 2013, entitled “An Improved Streaming Methodand System for Processing Network Metadata”, which application is anon-provisional of provisional application No. 61/751,243 filed on Jan.10, 2013, same title, which applications are incorporated herein intheir entirety by this reference.

BACKGROUND

A system and method for detecting and classifying network trafficanomalies is disclosed. The system includes an input module receiving adata stream containing information related to the network traffic, oneor a plurality of the data stream analyzers and a correlation module.The correlation module receives results of the data stream analysis bythe analyzers and determines if the potential anomaly is false or true.

REFERENCES

-   Jackson Higgings, K., What a DDoS Can Cost, Information Week Dark    Reading, May 5, 2012-   Lin, D., Network Intrusion Detection and Mitigation against Denial    of Service Attack, University of Pennsylvania, 2013-   Mamdani, E. H., Application of Fuzzy Logic to Approximate Reasoning    Using Linguistic Synthesis, IEEE Transactions on Computers, vol. 26,    no. 12, pp. 1182-1191, December 1977-   Sugeno, Michio, Advances in Fuzzy Measures: Theory and Applications,    First International Conference on Fuzzy Information processing,    Hawaii, July 1984-   Zadeh L. A., Kacprzyk, J., Computing with Words, Physica-Verlag,    Heidelberg, 1999-   Basseville, M., Nikiforov, I., Detection of Abrupt Changes: Theory    and Application, Prentice Hall, 1993-   Chui, Charles K., An Introduction to Wavelets, Academic Press, 1992-   Cortes, C., Vapnik, V., Support-vector networks, Machine Learning,    v.20, pp. 273-297, 1995-   Markovsky, I., Van Huffel S., Overview of total least squares    methods, Signal process, v.87, pp. 2283-2302, 2007-   Shannon, Claude E., A Mathematical Theory of Communication,    University of Illinois Press, 1949-   Martin, Nathaniel F. G., England, James W., Mathematical Theory of    Entropy, Cambridge University Press, 2011

FIELD OF THE INVENTION

In general the present invention relates to network monitoring and eventmanagement. More specifically, it relates to processing of networkmetadata obtained through network monitoring, which may efficientlyresult in useful information being reported in a timely manner to aconsumer of the metadata.

Network monitoring is a critical information technology (IT) functionoften used by Enterprises and Service Providers, which involves watchingthe activities occurring on an internal network for problems related toperformance, misbehaving hosts, suspicious user activity, etc. Networkmonitoring is made possible due to the information generated andprovided by various network devices. The information has been generallyreferred to as network metadata, i.e., a class of information describingactivity on the network which is supplemental and complementary to theprimary information traffic transmitted over the network.

Syslog is one type of network metadata commonly used for networkmonitoring. Syslog has become a standard format for logging programmessages and provides devices which would otherwise be unable tocommunicate a means to notify administrators of problems or performance.Syslog is often used for computer system management and securityauditing as well as generalized informational, analysis, and debuggingmessages. It is supported by a wide variety of devices (like printersand routers) and receivers across multiple platforms. Because of this,syslog can be used to integrate log data from many different types ofdevices in a computer system into a central repository.

More recently, another type of network metadata, referred to by variousvendors as NetFlow, jFlow, sFlow, etc., has also been introduced as apart of standard network traffic (hereafter generally referred to as“NetFlow”.) NetFlow is a network protocol for collecting IP trafficinformation that has become an industry standard for traffic monitoring.NetFlow can be generated by a variety of network devices such asrouters, switches, firewalls, intrusion detection systems (IDS),intrusion protection systems (IPS), network address translation (NAT)entities and many others. However, until recently, NetFlow networkmetadata was used exclusively for post factum network supervisionpurposes such as network topology discovery, locating network throughputbottlenecks, Service Level Agreement (SLA) validation, etc. Such limiteduse of NetFlow metadata can generally be attributed to the high volumeand high delivery rate of information produced by the network devices,the diversity of the information sources and an overall complexity ofintegrating additional information streams into existing eventanalyzers. More particularly, NetFlow metadata producers have typicallygenerated more information than consumers could analyze and use in areal time setting. For example, a single medium to large switch on anetwork might generate 400,000 NetFlow records per second.

Today's syslog collectors, syslog analyzers, security informationmanagement (SIM) systems, security event management (SEM) systems,security information and event management (SIEM) systems, etc.(collectively hereafter referred to as an “STEM system”) are eitherincapable of receiving and analyzing NetFlow, are limited to processingrudimentary information contained in NetFlow packets, or process NetFlowpackets at rates much lower than such packets are typically generated.

The advent of robust network monitoring protocols such as NetFlow v9(RFC 3954) and IPFIX (RFC 5101 and related IETF RFC) drastically expandsthe opportunity to use network metadata in the realm of network securityand intelligent network management. At the same time, due to theconstraints identified above, today's SIEM systems are not generallycapable of utilizing network monitoring information beyond simplyreporting observed byte and packet counts.

Anomaly detection on a computer network is the identification of items,events or behavior which differ from an expected, desired or normalpattern. When studied in the context of the network traffic, anomalydetection can be broadly classified into two categories:

a) network traffic anomalies in a neutral operational environment and

b) network traffic anomalies in the presence of malicious actors.

Type (a) network traffic anomalies occur under normal operationalconditions due to naturally overloaded or defective network devices or“flash mobs”—benign events when network traffic sufficiently increasesdue to an influx of legitimate network users.

Type (b) events may be caused by external forces and can be malicious innature. There is a number of ways that an attacker may cause maliciousnetwork anomalies but the Denial of Service (DoS) attack and its variantDistributed Denial of Service (DDoS) attack are by far the most commonand easiest to stage. With the DoS attack, the attacker's purpose is tomake one or more network resources inaccessible to legitimate users andthus to disrupt the activities of an organization. According to a 2012survey of 1,000 IT professionals, each hour in which their organizationis subject to a DDoS attack costs a victim organization between $10,000and $50,000 in lost revenue.

In order to avoid significant loss of business and revenue, networktraffic anomalies of both types should be detected, classified and madeknown in a timely manner to the network operators. The problem ofnetwork outages becomes even more severe in industrial and militarynetworks, when a loss of communications may cause catastrophicconsequences.

However, network anomaly detection becomes exceptionally hard when thenumber of items under observation increases along with the complexity ofeach observed item. Detecting anomalies in network traffic is one of theextreme examples of a complex anomaly detection problem.

A traditional approach to network anomaly detection requires creating ahistoric baseline pattern which is compared to the current pattern whenassessing deviation from a normal behavior. This traditional approach isinherently problematic in lieu of the following considerations:

-   -   Network traffic is dynamic and there is rarely a single pattern        which describes its temporal characteristics. This may lead to        the complex task of creating a great number of time bounded        historic baselines, which become almost immediately outdated        after a slight change in the operational environment.    -   The network itself is dynamic because, invariably, new devices        are installed, old devices are removed and operating devices may        be taken down for maintenance. Upon each change, an earlier        established baseline loses its validity and has to be        reestablished to adjust to each new network configuration.    -   Trends such as software virtualization, Software Defined        Networks (SDNs) and Network Function Virtualization (NFV)        further increase the dynamic nature of the network by creating        transient virtual networks capable of migrating across the        physical network. Even in the absence of any physical network        changes, instantiation of new network traffic producers and        consumers immediately invalidates established traffic baselines.    -   A high rate of change in the network ecosystem makes comparison        of the current network characteristics with historic data error        prone and highly susceptible to false positives.

Two approaches are known to have been previously utilized to attempt todetect DoS and DDoS attacks; a (a) a signature based approach, and (b) abaseline traffic based approaches. A recent University of Pennsylvaniastudy reported that certain of today's DDoS detection systems (Snort,PHAD, MADAME and MULTOPS) operate at low efficiency levels. Inparticular, the study articulated a low detection rate of unknown DDoSattacks by signature based systems (Snort, MADAME), a high false alarmrate of the systems relying on the baseline traffic information (PHAD)or any upfront assumptions concerning the traffic profiles (MULTOPS) anda requirement to completely re-train the systems relying on the baselinetraffic information (PHAD) when the traffic changes.

SUMMARY OF THE INVENTION

The present invention solves many of the problems associated with thetraditional baseline anomaly detection approach by eliminating relianceon historically established baselines or upfront assumptions. Instead,embodiments of the invention are able to identify anomalies by detectingmomentary changes and evaluating the trends in the observed trafficcharacteristics.

Embodiments of the present invention introduce a novel approach bycomputing trends in the observed network traffic characteristics and, incase of a multi-dimensional universe of discourse, computinghigher-level trends in the observed network traffic characteristics andclassifying the computed higher-level trends into linguistic categorieseasily understood by humans.

Embodiments of the present invention reduce the number of falsepositives experienced in network anomaly detection by assessing aplurality of the network traffic characteristics at once, applying aplurality of mathematical anomaly detection methods to the plurality ofnetwork traffic characteristics and applying a fuzzy logic-based modelof the network node health to the results produced by the plurality ofmathematical anomaly detection methods.

Embodiments of this invention are able to track abnormal trafficpatterns in important network junctures such as networking devices'interfaces, assess current health of the network devices and thenetworking infrastructure as a whole and determine the trend of thenetwork elements health thus predicting possible network failures. Basedon the network health trend analysis, the operator is capable ofoptimizing the network resources and avoiding outages. Alternatively,network optimization or maintenance decisions may be made automatically.

Embodiments of this invention are further able to identify importantnetwork security problems, such as detection of Denial of Service (DoS)and Distributed Denial of Service (DDoS) attacks in real time. Timelyattack detection permits automatic or manual alerting of a mitigationsystem to such attack when the confidence level of attack detection issufficient, as determined or pre-determined by the operator.

Embodiments of this invention may operate in the streaming fashionwithout resorting to a post factum analysis and provide informationabout critical network elements condition in real time. In suchembodiments, said stream of information packets may be permanentlystored only after applying at least one analytical algorithm to computea metric for assessing operational condition of the network fordetermining if a network anomaly exists. It should be appreciated thatthe method disclosed in this invention is applicable to the network dataat rest as well.

Embodiments of this invention may also be quickly deployed into servicebecause they do not impose upon the operator a slow and costly baselinetraffic information acquisition pre-process.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the present invention may be more clearly ascertained,some embodiments will now be described, by way of example, withreference to the accompanying drawings, in which:

FIG. 1 illustrates an exemplary simple computer network in which networkdevices such as, but not limited to, routers 103 and switches 102provide connectivity between end points such as physical host computers,101, or virtual machines 104 executing on the physical host computers101;

FIG. 2 illustrates an exemplary computer network as shown on FIG. 1,with the addition of a traffic data analyzer, NetFlow Integrator (“NFI”)110, which receives and analyzes network traffic data in the streamingmode and a generic back end system 111, which receives results of theanalysis;

FIG. 3 illustrates exemplary fuzzy classifiers that may be used tocharacterize network traffic flowing through a network node;

FIG. 4 illustrates an exemplary fuzzy inference matrix mapping observedtraffic parameters linguistic values to the Attention Level universe ofdiscourse;

FIG. 5 provides an exemplary result of the attention level computationat a constant traffic volume and variable relative packet rate in 5%increments;

FIG. 6 illustrates a result of the CUSUM algorithm applied to a sampleobserved data set;

FIG. 7 illustrates a result of the wavelet transform applied to a sampleobserved data set;

FIG. 8 illustrates a result of the SVM method applied to sample observeddata set;

FIG. 9 illustrates an exemplary embodiment of the invention, in whichchanges in two data observation domains, A and B, are evaluated;

FIG. 10 illustrates an exemplary embodiment of the disclosed invention,in which the network node health trend is evaluated using assessednetwork Node Health Score (NHS) values between data collection intervalsdtk and dtcur;

FIG. 10(a) illustrates an exemplary embodiment of the disclosedinvention, in which network node health trend is expressed using abest-fit line to illustrates a case in which the network node healthtrend falls into the Falling category;

FIG. 10(b) illustrates an exemplary embodiment of the disclosedinvention, in which the network Node Health Score (NHS) is broadened toquantify the health of a network device as a whole;

FIG. 10(c) illustrates an exemplary embodiment of the disclosedinvention, in which the network Node Health Score (NHS) is broadened toquantify the health of a network service;

FIG. 11 illustrates an embodiment of the invention applied to detectingand reporting DDoS attacks while minimizing the number of falsepositives;

FIG. 12 illustrates an embodiment of the invention in which, after afirst change point from the start of the observation process isdetected, the count of observed incomplete TCP/IP sessions may become aninitial baseline value for subsequent measurements;

FIG. 13 illustrates an embodiment of the invention in which, after oneor a plurality of change points is detected, the next K observationsimmediately following a rightmost detected change point may be checkedfor exceeding a pre-configured threshold value over the current baselinevalue;

FIG. 14 illustrates an embodiment of the invention in which, when asubsequent change point is detected, a new baseline value may beestablished and a previous current baseline value may be pushed on thestack of known baseline values;

FIG. 15 illustrates an embodiment of the invention in which an Agent mayreport the number of observed incomplete TCP/IP sessions over areporting interval;

FIG. 16 illustrates an embodiment of the invention in which steps aretaken to assess the nature of a network traffic anomaly;

FIG. 17 illustrates an embodiment of the invention in which a datacollection interval dtk may be designated as a network traffic anomalyif a change point is detected in the new IP addresses arrival rate and achange point is detected in the flows count in data collection intervalsdtk dtk−1 or dtk+1;

FIG. 18 illustrates an embodiment of the invention in which steps aretaken to assess the nature of a network traffic anomaly detectedaccording to the embodiment of FIG. 17;

FIG. 19 illustrates an embodiment of the invention in which steps aretaken to track entropy deviations;

FIG. 20 illustrates an embodiment of the invention in which, when one ormore change points are detected on an observation interval, an Agenttakes the latest detected change point and computes entropy value trend,starting at the data collection interval when the change point wasdetected to the current data collection interval; and

FIG. 21 illustrates an embodiment of the invention that computes acumulative anomaly confidence metric which takes into consideration thelatest report and previous events reported by that flow informationsource.

DETAILED DESCRIPTION OF THE INVENTION

In general, the present invention relates to network monitoring andevent management. More specifically it relates to processing networkmetadata obtained as a result of network monitoring activities andsubsequent processing of the metadata, which may result in usefulinformation being reported to an operator and/or event management systemin a timely manner.

The present invention will now be described in detail with reference toseveral embodiments thereof as illustrated in the accompanying drawings.In the following description, numerous specific details are set forth inorder to provide a thorough understanding of embodiments of the presentinvention. It will be apparent, however, to one skilled in the art, thatembodiments may be practiced without some or all of these specificdetails. In other instances, well known process steps and/or structureshave not been described in detail in order to not unnecessarily obscurethe present invention. The features and advantages of embodiments may bebetter understood with reference to the drawings and discussions thatfollow.

Aspects, features and advantages of exemplary embodiments of the presentinvention will become better understood with regard to the followingdescription in connection with the accompanying drawing(s). It should beapparent to those skilled in the art that the described embodiments ofthe present invention provided herein are illustrative only and notlimiting, having been presented by way of example only. All featuresdisclosed in this description may be replaced by alternative featuresserving the same or similar purpose, unless expressly stated otherwise.Therefore, numerous other embodiments of the modifications thereof arecontemplated as falling within the scope of the present invention asdefined herein and equivalents thereto. Hence, use of absolute and/orsequential terms, such as, for example, “will,” “will not,” “shall,”“shall not,” “must,” “must not,” “first,” “initially,” “next,”“subsequently,” “before,” “after,” “lastly,” and “finally,” are notmeant to limit the scope of the present invention as the embodimentsdisclosed herein are merely exemplary.

In the following description, the invention is disclosed in the contextof network metadata processing for the purposes of illustration only.However, it will be appreciated that the invention is suitable for abroader variety of applications and uses and certain embodiments of theinvention are applicable in contexts other than network metadataprocessing. For example, methods disclosed herein may be applied tocontrolling city traffic flow by regulating duration of the trafficlights at the intersections. In yet another example, methods disclosedherein may be suitable for controlling a power grid. It is alsoappreciated that the methods disclosed herein are applicable withoutlimitations related to the actual network traffic itself.

In one embodiment of this invention, the method and system may beimplemented using one or more instances of NetFlow Integrator (“NH”)—asoftware program which enables the integration of NetFlow traffic inversions 1 through 8, NetFlow v9, jFlow, sflowd, sFlow, NetStream, IPFIXand similar (“NetFlow”) traffic with any system capable of storingand/or processing network metadata in syslog format. The integration maybe achieved by converting network metadata generated by the NetFlowproducers on the network into a lingua franca of network monitoringsystems—syslog. Mapping of the NetFlow information to correspondingsyslog information may be performed according to policies, rules andpriorities established by the NFI Administrator.

It should be appreciated that the use of syslog for reporting networkmetadata is exemplary and other data presentation and delivery methodssuch as, without limitation, CEF or JSON, may be used.

Network Health Assessment

FIG. 1 illustrates an exemplary simple computer network in which networkdevices such as, but not limited to, routers 103 and switches 102provide connectivity between end points such as physical host computers,101, or virtual machines 104 executing on the physical host computers101.

A typical computer network is a complex system in which reliabilitydeclines as the number of the networked devices and volume of networktraffic increases. And as the network size increases, the task ofunderstanding the network state and assessing the condition ofindividual network nodes becomes both more difficult and more important.

FIG. 2 illustrates an exemplary computer network as shown on FIG. 1 withthe addition of a traffic data analyzer, NetFlow Integrator (“NFI”) 110,which receives and analyzes network traffic data in the streaming modeand a generic back end system 111, which receives results of theanalysis. In one embodiment of this invention, information about thenetwork traffic is collected using the NetFlow protocol 112.

In one embodiment of the invention, a network node health assessmentmethod may adhere to the following procedure:

1. Establish a traffic data collection interval, dt. In one embodimentof the method, the data collection interval length may be selected to bebetween 10 and 60 sec.

2. Establish a traffic observation interval, T. T is a multiple of dt:T=N*dt. In one embodiment of the method, traffic observation intervallength is selected to be between 20 to 40 multiples of dt. In anexemplary embodiment which utilizes a change detection method based onthe wavelet series algorithm, the multiple is N=2n due to the wavelettransform requirements. Thus in one embodiment of the method, N=32 maybe selected.

3. Collect multivariate information about traffic passing through anetwork interface over a data information collection interval, dt. In anexemplary embodiment, the plurality of collected network trafficparameters is comprised without limitation of traffic volume and packetrate measurements.

4. The observations may be repeated N times: R1, . . . , RN, (withRi—being information collected during i-th interval).

5. At the end of the collection time interval N+1, at least one changedetection method is applied to the time series R2, . . . , RN+1 directlyor, in case of the wavelet transform, in conjunction with the timeseries R1, . . . , RN used as a template.

6. Identify change points detected by each applied change detectionmethod and compute network Node Health Score (“NHS”) for each datacollection interval, starting from the rightmost identified change pointdata collection interval to the current data collection interval.

7. Estimate a network interface NHS value as a NHS value computed forthe current data collection interval.

8. Estimate a network interface NHS trend, based upon a NHS valuevariation pattern over data collection intervals starting from therightmost identified change point data collection interval to thecurrent data collection interval.

It should be appreciated that the duration of a data collectioninterval, dt, is a configurable parameter and may be fine-tuneddepending on the user's desired degree of awareness and/or precision. Byselecting a shorter data collection interval, the user may benefit fromearlier notifications about a negative NHS value trend, at the cost ofpossibly receiving extraneous notifications about short spikes in thenetwork traffic that may not be significant. Selection of a prolongeddata collection interval filters out short network traffic spikes butmay delay delivery of notifications.

It should be appreciated that a significant benefit of this embodimentof the invention stems from contextual monitoring of the node healthtrend which enables assessing the network failure risk. It should alsobe appreciated that the disclosed method can be fully operational afterN+1 data collection intervals, which in practice may span as little as15-20 minutes. Accordingly, the method and system of this embodiment maybe deployed and become operable almost immediately, without delaysassociated with establishing long and unreliable historical baselines.

Observed Data Pretreatment

It should be appreciated that short-lived, sharp changes in the observednetwork data may lead to an increased appearance of false positives. Inorder to mitigate this potential problem, an exponential smoothingprocess may be applied to the observed data:

{tilde over (X)}(i)=αX(i)+(1−α){tilde over (X)}(i−1), {tilde over(X)}(0)=X(0), in which

{tilde over (X)}(i)—is the smoothed i-th observation value

X(i)—is the actual i-th observation value

α—is the smoothing coefficient. In an exemplary implementation, thesmoothing coefficient may be α=0.35, or other value preferred by theoperator, based upon factors such as a desire for more reliableindications versus a desire for earlier indications of potential networktrouble.

Network Node Health Score (NHS)

In the disclosed embodiment, the Node Health Score (NHS) may be a singlemetric which provides guidance about the condition of a particularnetwork node, such as a network device interface. FIG. 3 illustratesexemplary fuzzy classifiers that may be used to characterize networktraffic flowing through a network node. The x-axis of each fuzzyclassifier may represent the relative value of the classified parameteron the [0, 1] interval where the value of 1 is reached when theparameter reaches its maximal value. The y-axis of each fuzzy classifiermay measure a degree to which a given parameter value belongs to acertain linguistic classification.

Referring to FIG. 3, the Traffic Volume fuzzy classifier may representlinguistic classifications of the traffic volume flowing through thenetwork node. The areas marked Low 120, Medium 121 and High 122correspond to the traffic levels which linguistically could becharacterized as low, medium and high, respectively.

Further referring to FIG. 3, the Packet Rate fuzzy classifier mayrepresent linguistic classifications of the rate at which the packetsare flowing through the network node. The areas marked Low 123, Medium124 and High 125 correspond to the packet rate levels whichlinguistically could be characterized as low, medium and high,respectively.

Further referring to FIG. 3, the Attention Level fuzzy classifier mayrepresent linguistic classifications of the operator's attention levelwhich the network node requires. The areas marked Low 126 and High 125correspond to the different degrees of the operator's attention whichlinguistically could be characterized as normal and troublesome,respectively.

In the first step of the network node health assessment, the disclosedmethod may input relative values of the observed network trafficcharacteristics (“crisp inputs”) through the node and find a degree towhich each of the observed values belongs to a certain linguisticcategory in its universe of discourse.

In the second step of the network node health assessment, the disclosedmethod may input the computed degree to which each of the observedvalues belongs to a certain linguistic category in its universe ofdiscourse and map these values onto the Attention Level fuzzy classifierusing a fuzzy inference matrix presented on FIG. 4. The method mayinclude a step of computing the required attention level value, AL,e.g., using Mamdani and Sugeno fuzzy inference methods. The Node HealthCoefficient may be computed as:

NHS=1−AL

Referring to FIG. 4, the exemplary fuzzy inference matrix may mapobserved traffic parameters linguistic values to the Attention Leveluniverse of discourse. In the exemplary fuzzy inference matrix shown inFIG. 4, the rows correspond to the observed relative packet rate valuesand the columns correspond to the observed relative traffic volumepassing through the node, wherein each cell represents a linguisticvalue of a corresponding fuzzy classification rule:

-   -   IF Traffic Volume is X AND Packet Rate is Y THEN Attention Level        is Z

For example, when the traffic volume through the network node islinguistically classified as low (“L”) and the rate of packets flowingthrough the network node is linguistically classified as low (“L”), thenthe attention level required to this node might be classified as high(“H”)—indicating a high probability that a lightly loaded network nodeis experiencing hardware problems.

FIG. 5 provides an exemplary result of the attention level computationat a constant traffic volume 140 and variable relative packet rate in 5%increments 141. The results of attention level computation scaled to theinterval [0, 100] using the Mamdani and the Sugeno methods are presentedin columns 142 and 143, respectively. In an exemplary implementation ofthe disclosed method, the effective attention level value, AL, iscomputed as an average of attention level value computed using theMamdani method, AL _(M), and the attention level value computed usingthe Sugeno method, AL_(S):

$\overset{\_}{AL} = \frac{{AL}_{M} + {AL}_{S}}{2}$

It should be appreciated that fuzzy classifiers shown on FIG. 3 areexemplary and may include other linguistic classifications such as“very” or have other linguistic classifications altogether. It should bealso appreciated that a fuzzy inference matrix shown on FIG. 4 isexemplary and without limitation may include additional linguisticclassifications and observation domains.

Computing Relative Node Load

Network Node Health Score (NHS) computation may involve expressingtraffic parameters in relative terms. In the exemplary embodiment of thedisclosed method, relative traffic volume passing through a network nodemay be computed as:

${\overset{\_}{T} = \frac{3B}{T_{\max \mspace{11mu} {dc}}}},$

in which

B—is the bidirectional IP Level 3 traffic volume through the networknode during the data collection interval, (in bytes)

T_(max□)—is the maximal nominal speed of the node, (in bits/sec “bps”),and

dt—is the data collection interval, (in sec).

In the exemplary implementation of the disclosed method, relative rateat which network packets flow through a network node may be computed as:

${R = \frac{T_{\max}}{s\overset{\_}{S}}},$

in which

S—is the average network packet size during the data collectioninterval, (in bytes):

${\overset{\_}{S} = {\frac{B}{P} + {L\; 2}}},$

in which

P—is the number of packets in both directions observed during the datacollection interval

For an IP-based network, Layer 2=41−the size of the Layer 2 header (17bytes), the size of the inter-frame gap (12 bytes) and the size of thepreamble (8 bytes). The size of the Layer 2 information may be taken asan average of a standard Layer 2 header (14 bytes), Layer 2 header witha VLAN tag (16 bytes) and a Layer 2 header with MPLS labels (20 bytes).

It should be appreciated that that the above computations are exemplaryand relative traffic volume and relative rate at which network packetsflow through a network node may be computed using a different formula.

Change Point Detection Algorithms

Change detection is a statistical analysis approach which attempts toidentify changes in the probability distribution of a stochastic processor time series. In general the change detection problem impliesdetecting whether or not one or more changes have occurred andidentifying the times of such changes.

In an exemplary embodiment, the Cumulative Sums (“CUSUM”) algorithm, thewavelet transform (“wavelet”) and the Support Vector Machine (“SVM”)algorithm may be applied to the observed traffic characteristics ofnetwork traffic flowing through a network node. The purpose of applyinga plurality of change point detection algorithms is to achieve aconfident detection of a change. It is appreciated that other changepoint detection algorithms may be applied to the observed networktraffic characteristics and the choice of the CUSUM, the wavelet and theSVM algorithms is exemplary.

Cumulative Sums (CUSUM) Algorithm

FIG. 6 illustrates a result of the CUSUM algorithm applied to a sampleobserved data set 151. Dots 150 indicate change points discovered by theCUSUM algorithm in the evaluated original data set 151. Furtherreferring to FIG. 6, in an exemplary rendering, the y-axis represents achange point confidence metric (0-100)—the closer this metric's value isto 100 the more confidently a change point has been identified.

Wavelet Transform Algorithm

FIG. 7 illustrates a result of the wavelet transform applied to sampleobserved data set 151. The wavelet transform of the original data 160normalizes observed values 151 around y=0 and filters out high frequencycomponents, commonly known as noise, in the observed values 151. Achange point is a point where an absolute value of the transformed dataexceeds a certain preset threshold value yT, |yT|=Δy, 162. It should beappreciated that change point detection probability increases with thelarger absolute value of the transformed data, yT.

Due to an artificial abrupt change at the start of the observationinterval, deviations identified on a few leftmost data collectionintervals, Δx, 163 are preferably discarded. Per algorithm definition,the number of the data collection intervals, N, to which the wavelettransform is applied should be equal to 2n, where n is a whole number(e.g., N=32, n=5).

Support Vector Machine (SVM) Algorithm

SVM is a classification method which identifies similarity of disparateobservations. SVM uses a first data set as a template and classifies asecond data set against a first data set. FIG. 8 illustrates a result ofthe SVM method applied to sample observed data set 151.

As with the wavelet transform illustrated on FIG. 7, the SVMclassification algorithm includes selection of a threshold value, Δy,172 for identifying abrupt changes in the transformed data 170. Itshould be appreciated that a threshold value 172 selected for the datatransformed by SVM algorithm 170 is unrelated to a threshold value 163selected for the wavelet transform algorithm.

Network Node Health Assessment

Referring to FIG. 9, in an exemplary embodiment of the disclosed method,a network node health metric may be computed for the data collectionintervals, dt, 182, starting with a rightmost detected change pointthrough the current data collection interval. FIG. 9 illustrates anexemplary embodiment of the method, in which changes in two dataobservation domains, A and B, are evaluated. The method disclosed hereinmay be extended without limitation to an arbitrary number of dataobservation domains.

Further referring to FIG. 9, if a case is encountered in which changepoints were detected in all observation domains, then NHS is computedfor the data collection domains starting from the rightmost datacollection interval on which a change point was detected. For example,referring to FIG. 9, if change point in domain A 180 was detected atdata collection interval dtk 183 and change point in domain B 181 wasdetected at data collection interval dtk-j 184, j>0, then NHS iscomputed for all data collection intervals between dtk and the currentdata collection interval inclusive.

In a case in which there is a change point in one data domain only, NHSmay be computed for all data collection intervals starting with therightmost data collection interval on which change point in the observeddata was detected to the current data collection interval inclusive. Ina case in which no change points were detected, NHS may be computed forthe current data collection interval only.

Network Node Health Trend Evaluation

Referring to FIG. 10, in an exemplary embodiment of the disclosedmethod, network node health trend is evaluated using assessed networkNode Health Score (NHS) values between data collection intervals dtk 183and dtcur 198, where data collection interval dtk 183 is a datacollection interval on which the last change point in the currentobservation interval 190 was observed and dtcur 198 is the current datacollection interval.

Further referring to FIG. 10, network node health trend may beassociated with the slope of a best-fit line 192 drawn between thepoints 191 representing network Node Health Score (NHS) values on eachdata collection time interval, starting with the data collection timeinterval 183, where the last change point in the current observationinterval 190 was detected. In an exemplary implementation, best-fit line192 is calculated using the Total Least Square (“TLS”) method. It shouldbe appreciated that a fitting method other than TLS could be used forthat purpose.

Further referring to FIG. 10, network node health trend may beclassified into distinct qualitative categories, e.g., called Peaking195, Improving 194, Neutral 193, Degrading 196 and Falling 197, based onthe slope of the best-fit line 192. In a degenerate case in which thelast change point in the current observation interval 190 was detectedduring the current data collection interval dtcur 198, the network nodehealth trend may be classified as Neutral.

Further referring to FIG. 10, the exemplary depicted network node healthtrend expressed using a best-fit line 192 illustrates a case when thenetwork node health trend falls into the Improving 194 category. TheImproving 194 trend classification is attributed to the fact ofcontinuing steady growth of the NHS values 191 since most recentobserved change point.

Referring to FIG. 10a , the exemplary depicted network node health trendexpressed using a best-fit line 199 illustrates a case when the networknode health trend falls into the Falling 197 category. The Falling 197trend classification is attributed to the fact of a precipitous drop ofthe Network Node Score (NHS) values 198 since most recent observedchange point.

Network Device Health Assessment

Referring to FIG. 10b the notion of the network Node Health Score (NHS)may be further broadened to quantifying health of a network device 400as a whole. In an exemplary embodiment of such computation NHS of anetwork device, NHSD, may be assessed as a minimal NHS of the devices'network nodes 401:

NHS_(D)=min(NHS∫), i=1, . . . ,n, where

NHS∫—Network Health Score of the i-th devices' node and

n—a total number of the network nodes deployed on the device.

Further referring to FIG. 10b the above exemplary quantification of anetwork device 400 NHS is justified by the fact that network nodes 401deployed on the device 400 are an integral part of the device 400 and,as such, are interdependent and performance of each network node 401 isaffected by performance of the other network nodes deployed on thedevice.

Further referring to FIG. 10b it should be appreciated that otherapproaches to quantification of a network device 400 health assessment,such as associating a relative weight of each network node 401 based onthe network node nominal throughput, may be considered.

Network Service Availability Assessment

Referring to FIG. 10c in the universe of discourse of this inventionnetwork devices 410 are one or a plurality of intermediaries, such as,without limitation, routers, switches and firewalls, which pass networktraffic to the network service 411. A network service 411 is anapplication running at the network application layer that provides dataprocessing, storage, presentation and other capabilities via applicationlayer network protocols.

Further referring to FIG. 10c , network service 411 availability isconditional on the availability of the network devices 410 which forwardnetwork traffic to the network service 411. In case of a network device410 poor performance or in a case of a network device 410 failureavailability of a network service is degraded or it becomesinaccessible. Due to the importance of the network services 411 to theenterprise business it is important to detect failures in the networkpaths leading to the network service 411 and quantify its availabilitybased on health of said network paths.

Further referring to FIG. 10c consider a network service 411 with mnetwork paths 412 over which network traffic is flowing to and from theservice. In the spirit of this invention availability of the networkservice 411 may be expressed using the network Node Health Score (NHS).In an exemplary embodiment the NHS value of a network service 411availability, NHS_(svc), may be computed as a weighted average of theNHS values of each network device 410 which forwards network traffic tothe said network service 411:

${{NHS}_{svc} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}{\omega_{i}{NHS}_{D}^{i}}}}},$

where

NHSb—Network Health Score of an i-th network device 410 which forwardsnetwork traffic to the network service 411

m—a total number of network devices 410 forwarding network traffic tothe network service 411

ωi—a share of the network traffic flowing to and from the networkservice 411 through each network device 410. The share is computed byobserving the network traffic flows to and from the network service 411through the i-th network device 410, Vi, summing up network trafficvolume in each flow over a data collection period and dividing it by atotal traffic volume to and from the network service 411, V, over thedata collection period:

${w_{i} = \frac{V_{i}}{V}},.$

Network service's NHS value computation provides a robust mechanism forreporting condition of the networked resources to the network operator.

It should be appreciated that other approaches to quantifying a networkservice availability, such as including nominal performance of thenetwork devices in the device weight computation, may be considered.

Detecting Anomalous Traffic

A Denial of Service (“DoS”) attack is an attempt to make a networkresource unavailable to its intended users. A Distributed Denial ofService (“DDoS”) attack is a variant of a DOS attack in which multiplecompromised systems are used to target a single system and cause a DoSattack.

During a DDoS attack, the incoming traffic flooding the target typicallyoriginates from many different sources, making it difficult todistinguish legitimate user traffic from the attack traffic spreadacross a great number of points of origin.

In an exemplary embodiment, a method disclosed herein may be applied todetecting and reporting DDoS attacks while minimizing the number offalse positives. Referring to FIG. 11, the disclosed method utilizesnetwork traffic description 212 which contains information about networktraffic and takes into account a plurality of the network trafficcharacteristics, each of which is evaluated by a plurality ofspecialized modules (“Agent”) 210. A system which implements thedisclosed method delivers information describing the network traffic toeach Agent 210 and each Agent 210 may treat such information in aspecialized fashion.

Further referring to FIG. 11, once an Agent 210 collects enoughinformation to make a conclusion (or in response to a timer) the Agent210 may report its findings to an Event Correlation Processor (“ECP”)211 which functions to make a final decision concerning whether theevent is positive or whether the event is negative.

It should be appreciated that the collection of algorithms implementedin Agents 210 which provide input into the ECP 211 final decision makingprocedure may vary and algorithms other than disclosed herein may beused for providing such input. It should also be appreciated that a listof Agents 210 discussed in the following text is for the purposes ofillustration only and does not constitute any limitations of thedisclosed method.

While an exemplary embodiment of the method as disclosed herein analyzesnetwork traffic throughout the network it should be appreciated that itmay, without a limitation, be applied to the network traffic to aparticular IP addresses thus detecting DDoS attacks targeting aparticular network service or a plurality of network services.

Agent A: TCP/IP Traffic Analyzer

Further referring to FIG. 11, Agent A may specialize in detectingTCP/IP-based DoS flood attacks and reporting observations to ECP 211.

TCP/IP flood attacks are one of the most ubiquitous classes of DDoSattacks seen in practice. The most common type of a TCP/IP flood attackis a SYN flood attack during which the attacker sends a flood of TCP/IPSYN packets with source IP addresses of subverted hosts or spoofed IPaddresses.

When a TCP/IP session is initiated between two communicating peers A andB, a standard TCP/IP SYN-SYN/ACK-ACK message exchange takes place. Theinitiator of this communication, A, first sends a TCP/IP SYN packet andthe responder, B, responds with a TCP/IP SYN/ACK packet. Under normalcircumstances, the initiator, A, then responds with a TCP/IP ACK packetand may start sending data to the responder, B. In a case in which theinitiator, A, is a malicious entity, it may withhold sending the TCP/IPACK packet to the responder, B, thus tying up the responder's resourcesallocated for creation of a new TCP/IP session.

It should be appreciated that other types of TCP/IP flood attacks, suchas reflected SYN/ACK flood and TCP/IP FIN flood, exist and the methoddisclosed herein is also applicable to those types of attacks withoutany limitations.

Agent A monitors the number of incomplete TCP/IP sessions—that is,initiated TCP/IP sessions in which the responder, B, did not receive aTCP/IP ACK packet in response to a sent TCP/IP SYN/ACK packet. Forexample, Agent A may compute the number of unanswered TCP/IP SYN/ACKresponses on each data collection interval, dt.

In an exemplary embodiment, in order to detect a nascent TCP/IP SYNflood attack, a sliding observation interval T=dt×N, N—a whole number,may be selected. Such selection creates a plurality of the observed datavalues changes in which are analyzed. The CUSUM algorithm may thereafterbe applied for identifying the change points in the series of the countsof incomplete TCP/IP sessions for each data collection interval, dt.

Referring to FIG. 12, when a first change point 220 from the start ofthe observation process is detected, the count of observed incompleteTCP/IP sessions may become an initial baseline value 221, b0, forsubsequent measurements. It should be appreciated that methods otherthan selection of a value corresponding to the first change point 220may be used for establishing an initial baseline value. The approachdescribed herein is exemplary and could be replaced by setting a fixedinitial baseline value, taking an average value of the data collectionintervals prior to the first change point 220, and/or other methods.

Referring to FIG. 13, in a case in which one or a plurality of changepoints is detected, the next K observations 222 immediately following arightmost detected change point 223 may be checked for exceeding apre-configured threshold value, δ, 224 over the current baseline value225. Agent A may report an event if, for example, one of the followingconditions are satisfied:

CK≥CK−1≥ . . . ≥C0  1)

ave(Ci)≥C0,i=1, . . . ,K  2)

in which

C0—is the number of incomplete TCP/IP sessions at change point 223

Ci—is the number of incomplete TCP/IP sessions during the i-thsuccessive data collection interval, dt, i=1, . . . , K

ave—is the average function.

Referring to FIG. 14, when a subsequent change point 230 is detected, anew baseline value 231 may be established and a previous currentbaseline value 225 may be pushed on the stack of known baseline values.

Referring to FIG. 15, Agent A may report the number of observedincomplete TCP/IP sessions over a reporting interval 240, which maystart at the first data collection interval when the number of observedincomplete TCP/IP sessions exceeded a current threshold value 241, untila detected abatement change point 243 when the number of observedincomplete TCP/IP sessions falls below the currently active baseline242.

Further referring to FIG. 15, when an abatement change point isdetected, a baseline value at the top of the stack of known baselinevalues may be extracted and its value then becomes a currently activebaseline 242. In a case in which the stack of known baseline values isnot empty, Agent A may continue reporting the number of observedincomplete TCP/IP sessions. If the stack of known baseline values isempty, Agent A may discontinue reporting.

Agent B: Generic Network Traffic Characteristics Analyzer

Agent B may be an intelligent analyzer of network trafficcharacteristics. Agent B may monitor and analyze traffic for the IPlayer protocols which cause an overwhelming majority of the DoS attacks.In an exemplary embodiment, Agent B monitors and analyzes networktraffic which utilizes the TCP/IP, UDP and ICMP protocols. It isappreciated that Agent B may monitor, without limitation, trafficcharacteristics of other IP layer protocols such as GRE, IGMP andothers.

When monitoring network traffic characteristics, Agent B operates onconsolidated units of information herein referred to as “flow”. In anexemplary embodiment, a flow is a unidirectional sequence of networkpackets characterized by an IP layer protocol and its source anddestination points. It is appreciated that other network trafficcharacteristics such as Type of Service (ToS), Autonomous System Number(ASN) or similar information may characterize a flow.

The following network traffic characteristics may be assessed for eachmonitored IP layer protocol for the duration of each collection timeinterval, dt:

-   -   1) average number of bytes per packet    -   2) average number of packets per flow    -   3) average number of bytes per flow, and    -   4) the number of unique source IP addresses.

In order to detect changes in the network traffic characteristics, asliding observation interval T may be selected:

T=dt×M, M=2^(n),

in which n is a whole number. At the end of the observation timeinterval T, the wavelet transform may be applied to each of the Mcollected sequences of characteristics (1)-(4). Collection timeintervals, dt, on which a transformed value of a characteristic exceedsa threshold, τ, are preferably marked as suspicious.

In the exemplary embodiment, the following function is used to computeconfidence value for each of collected network traffic characteristics(1)-(4):

${C_{j}( X_{i} )} = \{ \begin{matrix}{0,} & {y_{i} < \tau} \\{{1 - \frac{\tau}{y_{i}}},} & {y_{i} \geq \tau}\end{matrix} $

in which X_(i)—is the i-th data collection interval dt, and

y_(i)—is the value of the transformed characteristic on the i-th timeinterval.

A correlated confidence metric may be calculated as a sum of individualconfidence values:

${\overset{\_}{C}( X_{i} )} = {\sum\limits_{j = 1}^{n}{C_{j}( X_{i} )}}$

Data collection intervals with the confidence metric exceeding a presetthreshold, σ, may be designated as a candidate point of a networktraffic anomaly.

In the exemplary embodiment, besides collecting network trafficcharacteristics (1)-(4), the following cumulative network trafficcharacteristics may be collected on each data collection interval, dt:

-   -   5) cumulative number of bytes    -   6) cumulative number of packets, and    -   7) cumulative number of observed flows.

Referring to FIG. 16, in order to assess the nature of the networktraffic anomaly, the most recent observed candidate change point may beselected and the trend may be computed for each of the network trafficcharacteristics (4)-(7), 250-253, then each computed trend line may betreated as a unit vector and the overall trend 254 may be computed as avector sum of network traffic characteristics trends 250-253.

Further referring to FIG. 16, in order to evaluate the overall trend 254of the network traffic characteristics, the overall trend 254 isclassified into a plurality of qualitative characteristics such as,without a limitation, Increasing 255, Sustainable 256 and Abating 257.If the overall trend 254 is classified into the Increasing 255qualitative category, then Agent B preferably reports a network trafficanomaly event, along with values of the current network trafficcharacteristics (4)-(7) and the overall trend slope value.

It should be appreciated that the overall trend classification maydiffer from the classification in the exemplary embodiment, such thatthe Sustainable 256 category is optional or the classification frameworkmay contain a greater number of qualitative categories.

Agent C: New IP Addresses Arrival Rate Analyzer

Since a DDoS attack is typically accomplished by means of subvertednetwork hosts or by spoofing the source IP address in the networkpackets used to stage the attack, the onset of a DDoS attack is oftencharacterized by an influx of previously unseen visitors. Agent C mayobserve changes in two observation domains in each data collectioninterval, dt:

-   -   8) new IP addresses arrival rate, and    -   9) the number of observed flows.

In order to detect changes in such network traffic characteristics, asliding observation interval T may be selected:

T=dt×N

in which N is a whole number and the CUSUM algorithm may be applied foridentifying change points in the rate of arrival of new IP addresses andthe observed flows count on each data collection interval dt.

Referring to FIG. 17, in an exemplary embodiment of the disclosedmethod, a data collection interval dtk 260 may be designated as anetwork traffic anomaly if a change point is detected in the new IPaddresses arrival rate 261 and a change point is detected in the flowscount 264 in data collection intervals dtk 260, dtk−1 262 or dtk+1 263.

It should be appreciated that by analyzing network trafficcharacteristics (8) and (9) as described herein, Agent C is able toprovide a mechanism for differentiating a DDoS attack from a phenomenoncalled “flash mob” which is attributed to an influx of legitimate users.A typical example of flash mob is an increased traffic to a commercialweb site when a new product is released or a spike in the networktraffic at the beginning of a work day.

Further referring to FIG. 17, Agent C may differentiate between a flashmob and a DDoS attack by imposing a requirement that a change point inthe flows count 264 in data collection intervals dtk 260, dtk−1 262 ordtk+1 263 be accompanied by a change point in the new IP addressesarrival rate 261. The reasoning for such dependency is that a legitimateuse of network resources results in longer interactions with networkresources, thus creating less flows than in case of a DDoS attack duringwhich a great number of flows is created.

Referring to FIG. 18, in order to assess the nature of the networktraffic anomaly, the most recent observed change point is selected andthe trend may be computed for each of the network trafficcharacteristics (8) 270 and (9) 271, then each computed trend line maybe treated as a unit vector and the overall trend 274 may be computed asa vector sum of network traffic characteristics trends 270 and 271.

Further referring to FIG. 18, in order to evaluate the overall trend 274of the network traffic characteristics, the overall trend 274 may beclassified into a plurality of qualitative characteristics such as,without a limitation, Increasing 275, Sustainable 276 and Abating 277.If the overall trend 274 is classified into the Increasing 275qualitative category, then Agent C preferably reports a network trafficanomaly event along with values of the current network trafficcharacteristics (8) and (9) and the overall trend slope value.

It should be appreciated that the overall trend classification maydiffer from the classification in the exemplary embodiment such that theSustainable 276 category is optional or the classification framework maycontain a greater number of qualitative categories.

Agent D: Traffic Entropy Analyzer

Entropy is a measure of unpredictability of information content. It mayalso be interpreted as a measure of chaos in a system. The entropy, H,is computed as:

$H = {- {\sum\limits_{i = 1}^{N}{p_{i}\log_{1}p_{i}}}}$

where

$p_{i} = \frac{n_{i}}{N}$

n_(i)—count of a given source IP address instance, N—total number ofobservations (N>0).

Since a DDoS attack is typically accomplished by means of subvertednetwork hosts or by spoofing the source IP address in the networkpackets used to stage the attack, the onset of a DDoS attack is oftencharacterized by an increase in the number of observed IP addresses and,in case of source IP addresses spoofing, a small number of each sourceIP address observations. Due to the above considerations, an entropyanalyzer provides a robust estimate of the network's informationconsistency.

In an exemplary embodiment, Agent D may compute entropy for each datacollection interval dt. Since entropy is a random variable, its mean μand deviation σ2 should be computed. In order to detect changes in theobserved network entropy a sliding observation interval T is preferablyselected:

T=dt×N

where N is a whole number and the CUSUM algorithm is applied foridentifying change points in the computed entropy value on each datacollection interval dt. Entropy mean and deviation σ may be computed atthe first detected change point but preferably not earlier than N shiftsof the observation interval T have taken place and first change pointcomputation is done after 2N×dt data collection intervals.

Referring to FIG. 19, in order to track entropy deviations, region μ±ασis designated as normal wherein μ 281 denotes mean and σ 282 denotesdeviation. Coefficient α defines a tolerance band 280 in which networktraffic content is considered adequate. For example, selecting α=2 283puts normal network traffic content into the 95 percentile andclassifies all other network traffic content as anomalous.

Referring to FIG. 20, when one or more change points are detected on anobservation interval, Agent D takes the latest detected change point andcomputes entropy value trend 290 starting at the data collectioninterval when the change point was detected to the current datacollection interval. Based on the slope of the trend line, the computedentropy value trend may be classified into the Steady 291, Increasing292 and Declining 293 qualitative categories. If the entropy valuecomputed for the current data collection interval is outside of thetolerance band and the trend is classified either as Increasing 292 orDeclining 293, then Agent D preferably reports a network traffic anomalyand a respective trend classification.

Agent D preferably repeats information about network anomaly and trendclassification for each subsequent data collection interval until theentropy value reenters the tolerance band. Upon the entropy valuereentering the tolerance band, Agent D preferably reports entropy valueabatement.

Network Traffic Anomaly Events Correlation Processor

Referring to FIG. 11, one of the fundamental advantages of the disclosedmethod is its capability of studying a plurality of aspects of thenetwork traffic description 212 simultaneously. The study isaccomplished by a plurality of specialized expert modules called Agents210. Each Agent analyzes a plurality of the network traffic parametersand makes a conclusion if the network traffic is anomalous in theAgent's universe of discourse. If the Agent finds an anomaly, it reportsits findings to the Event Correlation Processor (ECP) 211.

In an exemplary embodiment, ECP 211 collates anomaly reports receivedfrom the Agents by an id of a network device which was a source of theflow information for the analysis. Upon receiving an anomaly report, ECPpreferably computes a cumulative anomaly confidence metric which takesinto consideration the latest report and previous events reported bythat flow information source.

Referring to FIG. 21, each reported event may be assigned a weight, w.Last reported event En observed by ECP at time to 300 may be assigned aweight w(tn)=1 303, where n is a sequence number of the reported event.For all previous reported events, the weight is preferably exponentiallydecayed 301:

W(t _(n-k))=e ^(−μ(t) ^(n) ^(-t) ^(n-k) ⁾, 0<k<n

where μ is an exponential decay constant.

The cumulative confidence metric is computed as a sum of weights of allobserved events:

$C = {\sum\limits_{i = 1}^{n}{w( t_{i} )}}$

For practical purposes, in an exemplary embodiment the cumulativeconfidence metric computation terminates when the weight of a previouslyreported event becomes less than 0.01 302. ECP 211 preferably alerts thenetwork operator when the cumulative confidence metric value, C, for aparticular flow information source exceeds a certain configurablethreshold, C.

It should be appreciated that each reported event may be assigned aweight, ω, based on the reported event type. In an exemplary ECPembodiment the cumulative confidence metric, C, may be computed as:

${C = {\sum\limits_{i = 1}^{n}{\omega \; {w( t_{i} )}}}},$

where

ω—is a weight associated with a particular type of event which tookplace at the time ti.

It also should be appreciated that an exemplary ECP disclosed herein iscapable of correlating network traffic anomalies across multiple networkdevices and issue alerts for a plurality of network devices whichexperience anomalous network traffic. In an exemplary embodiment of suchcorrelation ECP may issue alerts for network devices in a certaingeographical region based on an IP blocks allocation database or groupedby one or a plurality of the Autonomous System Numbers (ASN).

Converging Network Traffic Anomalies Detection and Network DevicesHealth

It should be appreciated that network traffic anomalies such as floodingDDoS attacks contribute to a lower network Node Health Score (NHS) of anetwork device. Referring to FIG. 4 it should be noted that increasednetwork traffic level 130 and packet rate 131 map to a high (“H”)attention level for a particular network node and thus decrease its NHSvalue which in turn decreases the overall network device's NHS value. Inan exemplary embodiment emergence of one or a plurality of the networkdevices with low NHS values may be considered as yet another eventincluded in a collection of events processed by the Event CorrelationProcessor (ECP).

It should also be appreciated that the input of the network deviceshealth information in the collection of events processed by the EventCorrelation Processor (ECP) may be manipulated by assigning a weight tothe network devices health information thus controlling influence of thenetwork devices health information on the anomalous network trafficidentification.

While this invention has been described in terms of several embodiments,there are alterations, modifications, permutations, and substituteequivalents, which fall within the scope of this invention. Althoughsub-section titles have been provided to aid in the description of theinvention, these titles are merely illustrative and are not intended tolimit the scope of the present invention.

It should also be noted that there are many alternative ways ofimplementing the methods and apparatuses of the present invention. It istherefore intended that the following appended claims be interpreted asincluding all such alterations, modifications, permutations, andsubstitute equivalents as fall within the true spirit and scope of thepresent invention.

1. A method for detecting and classifying network traffic anomalies,comprising: receiving a packet of information related to networktraffic; passing said packet to one or a plurality of network trafficanalyzers; at least some of said network traffic analyzers capable ofapplying an analytical algorithm to information contained in said packetthat is different from the analytical algorithm applied by another ofsaid network traffic analyzers; receiving results of analysis performedby said analyzers; evaluating results of analysis performed by saidanalyzers as a collection; determining if the result of evaluationsignifies a network traffic anomaly; and emitting an alert if the resultof evaluation signifies a network traffic anomaly.
 2. A method as setforth in claim 1, further comprising the step of performing trendanalysis upon the results of said evaluating step to reduce falsepositives.
 3. A method for detecting and classifying network trafficanomalies, comprising: receiving a stream of packets of informationrelated to network traffic; passing at least a portion of said stream ofinformation packets to a network traffic analyzer; applying at least oneanalytical algorithm to said portion of said stream of informationpackets; determining if said applying step indicates the existence of anetwork traffic anomaly; and emitting an alert if a network trafficanomaly is detected; wherein said applying and said determining step arepracticed prior to any step of permanently storing said portion of saidstream of information packets.
 4. A method for assessing the conditionof an interface of a network device, comprising: receiving a stream ofpackets of information related to network traffic passing through saidnetwork device interface; passing at least a portion of said stream ofinformation packets to a network traffic analyzer; applying at least oneanalytical algorithm to said portion of said stream of informationpackets; wherein said applying step computes a metric for assessingoperational condition of said network device interface; emitting analert if said computed metric indicates an abnormal operationalcondition of said network device interface; wherein said applying andsaid metric computation are practiced prior to any step of permanentlystoring said portion of said stream of information packets.
 5. A methodas set forth in claim 4, further comprising the step of performing trendanalysis upon the results of said metric computation to predict a riskof failure of said network device interface.
 6. A method as set forth inclaim 4, further comprising the step of assessing the operationalcondition of the said network device as a function of assessedoperational condition of the interfaces of said network device; whereinsaid assessing step is practiced prior to any step of permanentlystoring said portion of said stream of information packets.
 7. A methodas set forth in claim 6, further comprising the step of assessing theoperational condition of a network service, comprising: receiving astream of packets of information related to network traffic passingthrough more than one network devices that forward said network trafficto and from said network service; passing at least a portion of saidstream of information packets to a network traffic analyzer; applyingsaid method to information packets pertaining to at least a portion ofsaid network devices that forward said network traffic to and from saidnetwork service; determining if said applying step indicates an abnormaloperational condition of at least one of said network devices; emittingan alert pertaining to a network service if said applying step indicatesan abnormal operational condition of at least one said network devicethat forwards said network traffic to and from said network service;wherein said determining and said emitting steps are practiced prior toany step of permanently storing said portion of said stream ofinformation packets. 8.-14. (canceled)